Use of Social Network Analysis to Study Crime
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4361400/
Rostami, Amir, and Hernan Mondani. “The Complexity of Crime Network Data: A Case Study of Its Consequences for Crime Control and the Study of Networks.” Ed. Thomas Niederkrotenthaler. PLoS ONE 10.3 (2015): e0119309. PMC. Web. 12 Sept. 2017.
I read an academic paper entitled The Complexity of Crime Network Data: A Case Study of Its Consequences for Crime Control and the Study of Networks by Amir Rostami and Hernan Mondani. This paper explores the use of social network analysis in criminal intelligence analysis, specifically with a case study of a Swedish street gang. The paper describes the use of social network analysis (SNA) with three different data sets – data sources, intelligence, and surveillance and co-offending data. These are the most common sources of information in criminal analysis, but the paper wanted to explore whether the use of these different network datasets would affect the social network analysis, and how that could affect the intelligence assessments. The researchers built networks from each of the three information sources, and compared them “by computing distance, centrality, and clustering measures”.
I found this article very interesting because this is the exact kind of research that I hope to do in my career. I am an information science major that is planning to pursue criminal intelligence analysis after college, so this article was a very interesting blending of the two fields.
This relates to the material because it shows how network graphs can be used in the field of intelligence analysis to study criminal patterns. This kind of social network analysis can be, and has been, used to find and understand patterns within terrorist networks, organized crime, and street gangs. So much intelligence could be discovered by studying the social network patterns within these criminal organizations, and it is so closely related to the social networks we learned about in class. There are many possibilities for using social networks and graph theory to study criminal organizations. For example, the nodes of a graph could represent the criminals within the organization, and the edges could represent the relationships between them, therefore giving criminal analysts the ability to make inferences about important people within the organization. Another example is that a social network graph could demonstrate connections between rival gangs by using positive and negative links.
Visual analytics would be extremely useful in SNAs like the ones described in this article, because graph theory is best described visually. When analyzing the patterns within the criminal organizations, a visual representation of the social network would be the most comprehensive way to detect obvious patterns and clusters within the network.